@article{hemelings2019artery,
  title={Artery-vein segmentation in fundus images using a fully convolutional network},
  author={Hemelings, Ruben and Elen, Bart and Stalmans, Ingeborg and Van Keer, Karel and De Boever, Patrick and Blaschko, Matthew B},
  journal={Computerized Medical Imaging and Graphics},
  volume={76},
  pages={101636},
  year={2019}
}

@INPROCEEDINGS{8036917, 
author={J. U. {Kim} and H. G. {Kim} and Y. M. {Ro}}, 
booktitle={EMBC}, 
title={Iterative deep convolutional encoder-decoder network for medical image segmentation}, 
year={2017}, 
pages={685-688}, 
}

@Article{Cheng2014,
	author="Cheng, Erkang
	and Du, Liang
	and Wu, Yi
	and Zhu, Ying J.
	and Megalooikonomou, Vasileios
	and Ling, Haibin",
	title="Discriminative vessel segmentation in retinal images by fusing context-aware hybrid features",
	journal="Machine Vision and Applications",
	year="2014",
	volume="25",
	number="7",
	pages="1779-1792",
}

@ARTICLE{7042289, 
	author={S. {Roychowdhury} and D. D. {Koozekanani} and K. K. {Parhi}}, 
	journal={IEEE Transactions on Biomedical Engineering}, 
	title={Iterative Vessel Segmentation of Fundus Images}, 
	year={2015}, 
	volume={62}, 
	number={7}, 
	pages={1738-1749}, 
}

@article{JIN2019,
title = "{DUNet}: A deformable network for retinal vessel segmentation",
journal = "Knowledge-Based Systems",
volume = "178",
pages = "149-162",
year = "2019",
author = "Qiangguo Jin and Zhaopeng Meng and Tuan D. Pham and Qi Chen and Leyi Wei and Ran Su"
}

@InProceedings{10.1007/978-3-030-00934-2_7,
author="Zhao, Yitian
and Xie, Jianyang
and Su, Pan
and Zheng, Yalin
and Liu, Yonghuai
and Cheng, Jun
and Liu, Jiang",
title="Retinal Artery and Vein Classification via Dominant Sets Clustering-Based Vascular Topology Estimation",
booktitle="MICCAI 2018",
year="2018",
pages="56--64",
}


 
 @inproceedings{10.1145/3354031.3354050,
 author = {Wang, Zhaolei and Lin, Junbin and Wang, Ruixuan and Zheng, Weishi},
 title = {Retinal Artery/Vein Classification via Rotation Augmentation and Deeply Supervised U-Net Segmentation},
 year = {2019},
 booktitle = {ICBIP 2019},
 pages = {71–76},
 numpages = {6},
 location = {Chengdu, China},
}

@article{woo2016associations,
  title={Associations of retinal artery occlusion and retinal vein occlusion to mortality, stroke, and myocardial infarction: a systematic review},
  author={Woo, SCY and Lip, GYH and Lip, PL},
  journal={Eye},
  volume={30},
  number={8},
  pages={1031-1038},
  year={2016},
  publisher={Nature Publishing Group}
}

@article{huang2018artery,
  title={Artery/vein classification using reflection features in retina fundus images},
  author={Huang, Fan and Dashtbozorg, Behdad and ter Haar Romeny, Bart M},
  journal={Machine Vision and Applications},
  volume={29},
  number={1},
  pages={23--34},
  year={2018}
}

@ARTICLE{5740926,
author={L. {Tang} and M. K. {Garvin} and K. {Lee} and W. L. W. {Alward} and Y. H. {Kwon} and M. D. {Abramoff}},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
title={Robust Multiscale Stereo Matching from Fundus Images with Radiometric Differences},
year={2011},
volume={33},
number={11},
pages={2245-2258},
}

@INPROCEEDINGS{6945076,
author={B. {Dashtbozorg} and A. M. {Mendonça} and S. {Penas} and A. {Campilho}},
booktitle={EMBC 2014},
title={RetinaCAD, a system for the assessment of retinal vascular changes},
year={2014},
volume={},
number={},
pages={6328-6331},
}

@InProceedings{li2019iternet,
    author = {Li, Liangzhi and Verma, Manisha and Nakashima, Yuta and Nagahara, Hajime and Kawasaki, Ryo},
    title = {IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks},
    booktitle = {IEEE WACV},
    year = {2020}
}

@article{GIRARD201996,
title = "Joint segmentation and classification of retinal arteries/veins from fundus images",
journal = "Artificial Intelligence in Medicine",
volume = "94",
pages = "96 - 109",
year = "2019",
author = "Fantin Girard and Conrad Kavalec and Farida Cheriet",
keywords = "CNN, Artery and vein classification, Vessel segmentation, Fundus images, Retina",
abstract = "Objective
Automatic artery/vein (A/V) segmentation from fundus images is required to track blood vessel changes occurring with many pathologies including retinopathy and cardiovascular pathologies. One of the clinical measures that quantifies vessel changes is the arterio-venous ratio (AVR) which represents the ratio between artery and vein diameters. This measure significantly depends on the accuracy of vessel segmentation and classification into arteries and veins. This paper proposes a fast, novel method for semantic A/V segmentation combining deep learning and graph propagation.
Methods
A convolutional neural network (CNN) is proposed to jointly segment and classify vessels into arteries and veins. The initial CNN labeling is propagated through a graph representation of the retinal vasculature, whose nodes are defined as the vessel branches and edges are weighted by the cost of linking pairs of branches. To efficiently propagate the labels, the graph is simplified into its minimum spanning tree.
Results
The method achieves an accuracy of 94.8% for vessels segmentation. The A/V classification achieves a specificity of 92.9% with a sensitivity of 93.7% on the CT-DRIVE database compared to the state-of-the-art-specificity and sensitivity, both of 91.7%.
Conclusion
The results show that our method outperforms the leading previous works on a public dataset for A/V classification and is by far the fastest.
Significance
The proposed global AVR calculated on the whole fundus image using our automatic A/V segmentation method can better track vessel changes associated to diabetic retinopathy than the standard local AVR calculated only around the optic disc."
}

@ARTICLE{7120990,
author={R. {Estrada} and M. J. {Allingham} and P. S. {Mettu} and S. W. {Cousins} and C. {Tomasi} and S. {Farsiu}},
journal={IEEE Transactions on Medical Imaging},
title={Retinal Artery-Vein Classification via Topology Estimation},
year={2015},
volume={34},
number={12},
pages={2518-2534},
keywords={biomedical optical imaging;blood vessels;eye;image classification;medical image processing;trees (mathematics);retinal artery-vein classification;graph-theoretic framework;fundus image;tree topology estimation framework;likelihood model;Veins;Arteries;Retina;Topology;Labeling;Image color analysis;Space exploration;Artery-vein classification;graph theory;image analysis;medical imaging;tree topology;Algorithms;Databases, Factual;Diagnostic Techniques, Ophthalmological;Humans;Image Processing, Computer-Assisted;Retinal Artery;Retinal Vein},
}

@INPROCEEDINGS{8759380,
author={A. {Galdran} and M. {Meyer} and P. {Costa} and {MendonÇa} and A. {Campilho}},
booktitle={ISBI 2019},
title={Uncertainty-Aware Artery/Vein Classification on Retinal Images},
year={2019},
volume={},
number={},
pages={556-560},
keywords={blood vessels;convolutional neural nets;eye;image classification;image segmentation;medical computing;medical image processing;retinal vessels;A/V classification;convolutional neural network;vessel segmentation;artery/vein classification;eye fundus images;retinal image acquisition devices;retinal image analysis;Retina;Training;Task analysis;Arteries;Veins;Image resolution;Uncertainty;Artery/Vein Classification;Retinal Vessel Segmentation;Uncertainty},
}

@ARTICLE{5876322,
author={M. {Niemeijer} and X. {Xu} and A. V. {Dumitrescu} and P. {Gupta} and B. {van Ginneken} and J. C. {Folk} and M. D. {Abramoff}},
journal={IEEE Transactions on Medical Imaging},
title={Automated Measurement of the Arteriolar-to-Venular Width Ratio in Digital Color Fundus Photographs},
year={2011},
volume={30},
number={11},
pages={1941-1950},
keywords={biomedical optical imaging;blood vessels;diseases;eye;medical image processing;automated measurement;arteriolar-to-venular width ratio;digital color fundus photograph;retinal arteries;retinal veins;cerebral atrophy;stroke;cardiovascular events;disease;retinopathy;optic disc;vessel segmentation;vessel width determination;skeletonization operation;bifurcation points;Optical imaging;Veins;Arteries;Image segmentation;Eyes;Feature extraction;Artery;artery vein ratio;artery vein separation;retina;vasculature;vein;Adult;Color;Diabetic Retinopathy;Diabetic Retinopathy;Fundus Oculi;Humans;Hypertensive Retinopathy;Hypertensive Retinopathy;Hypertensive Retinopathy;Optic Disk;Optic Disk;Photography;Radiographic Image Interpretation, Computer-Assisted;Reference Standards;Retina;Retina;Retinal Artery;Retinal Artery;Retinal Artery;Retinal Vein;Retinal Vein;Retinal Vein;Retinal Vessels;Retinal Vessels;Vascular Resistance},
doi={10.1109/TMI.2011.2159619},
}

@ARTICLE{6517259,
author={B. {Dashtbozorg} and A. M. {Mendonça} and A. {Campilho}},
journal={IEEE Transactions on Image Processing},
title={An Automatic Graph-Based Approach for Artery/Vein Classification in Retinal Images},
year={2014},
volume={23},
number={3},
pages={1073-1083},
keywords={blood vessels;cardiovascular system;diseases;eye;graph theory;image classification;image segmentation;medical image processing;automatic graph-based approach;artery-vein classification;retinal images;retinal vessel classification;vascular changes;systemic diseases;diabetes;hypertension;cardiovascular conditions;A-V classification;graph extraction;vascular tree;intersection point;graph nodes;vessel segment;graph links;graph-based labeling;VICAVR databases;DRIVE databases;INSPIRE-AVR databases;Artery/vein classification;graph;retinal images;vessel segmentation;Algorithms;Artificial Intelligence;Humans;Image Enhancement;Image Interpretation, Computer-Assisted;Pattern Recognition, Automated;Reproducibility of Results;Retinal Artery;Retinal Diseases;Retinal Vein;Retinoscopy;Sensitivity and Specificity},
}

@article{HUANG2018197,
title = "Retinal artery/vein classification using genetic-search feature selection",
journal = "Computer Methods and Programs in Biomedicine",
volume = "161",
pages = "197 - 207",
year = "2018",
author = "Fan Huang and Behdad Dashtbozorg and Tao Tan and Bart M. ter Haar Romeny",
keywords = "Fundus image, Artery/vein classification, Genetic search feature selection",
abstract = "Background and objectives: The automatic classification of retinal blood vessels into artery and vein (A/V) is still a challenging task in retinal image analysis. Recent works on A/V classification mainly focus on the graph analysis of the retinal vasculature, which exploits the connectivity of vessels to improve the classification performance. While they have overlooked the importance of pixel-wise classification to the final classification results. This paper shows that a complicated feature set is efficient for vessel centerline pixels classification. Methods: We extract enormous amount of features for vessel centerline pixels, and apply a genetic-search based feature selection technique to obtain the optimal feature subset for A/V classification. Results: The proposed method achieves an accuracy of 90.2%, the sensitivity of 89.6%, the specificity of 91.3% on the INSPIRE dataset. It shows that our method, using only the information of centerline pixels, gives a comparable performance as the techniques which use complicated graph analysis. In addition, the results on the images acquired by different fundus cameras show that our framework is capable for discriminating vessels independent of the imaging device characteristics, image resolution and image quality. Conclusion: The complicated feature set is essential for A/V classification, especially on the individual vessels where graph-based methods receive limitations. And it could provide a higher entry to the graph-analysis to achieve a better A/V labeling."
}

% Encoding: UTF-8

@InProceedings{UNet,
  author    = {Ronneberger, Olaf and Fischer, Philipp and Brox, Thomas},
  title     = {U-Net: Convolutional Networks for Biomedical Image Segmentation},
  booktitle = {MICCAI 2015},
  year      = {2015},
  pages     = {234--241},
  abstract  = {There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net.},
}

@Article{8598955,
  author   = {C. L. {Srinidhi} and P. {Aparna} and J. {Rajan}},
  title    = {Automated Method for Retinal Artery/Vein Separation via Graph Search Metaheuristic Approach},
  journal  = {IEEE Transactions on Image Processing},
  year     = {2019},
  volume   = {28},
  number   = {6},
  pages    = {2705-2718},
}

@Article{8341481,
  author   = {Z. {Yan} and X. {Yang} and K. {Cheng}},
  title    = {Joint Segment-Level and Pixel-Wise Losses for Deep Learning Based Retinal Vessel Segmentation},
  journal  = {IEEE Transactions on Biomedical Engineering},
  year     = {2018},
  volume   = {65},
  number   = {9},
  pages    = {1912-1923},
  keywords = {Image segmentation;Machine learning;Retinal vessels;Manuals;Loss measurement;Skeleton;Training;Segment-level loss;deep learning;retinal image analysis;vessel segmentation},
}

@Article{girard2019joint,
  author    = {Girard, Fantin and Kavalec, Conrad and Cheriet, Farida},
  title     = {Joint segmentation and classification of retinal arteries/veins from fundus images},
  journal   = {Artificial intelligence in medicine},
  year      = {2019},
  volume    = {94},
  pages     = {96--109},
}

@InProceedings{7950512,
  author    = {A. {Dasgupta} and S. {Singh}},
  title     = {A fully convolutional neural network based structured prediction approach towards the retinal vessel segmentation},
  booktitle = {2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)},
  year      = {2017},
  pages     = {248-251},
  keywords  = {blood vessels;eye;image segmentation;medical image processing;neural nets;fully convolutional neural network;retinal vessel segmentation;multilabel inference task;automatic retinal blood vessel segmentation;Image segmentation;Biomedical imaging;Blood vessels;Neural networks;Retinal vessels;Feature extraction;Computer-aided diagnosis;retinal vessels;convolution neural networks;image segmentation},
}

@Article{MOCCIA201871,
  author   = {Sara Moccia and Elena De Momi and Sara El Hadji and Leonardo S. Mattos},
  title    = {Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics},
  journal  = {Computer Methods and Programs in Biomedicine},
  year     = {2018},
  volume   = {158},
  pages    = {71 - 91},
  abstract = {Background
Blood vessel segmentation is a topic of high interest in medical image analysis since the analysis of vessels is crucial for diagnosis, treatment planning and execution, and evaluation of clinical outcomes in different fields, including laryngology, neurosurgery and ophthalmology. Automatic or semi-automatic vessel segmentation can support clinicians in performing these tasks. Different medical imaging techniques are currently used in clinical practice and an appropriate choice of the segmentation algorithm is mandatory to deal with the adopted imaging technique characteristics (e.g. resolution, noise and vessel contrast).
Objective
This paper aims at reviewing the most recent and innovative blood vessel segmentation algorithms. Among the algorithms and approaches considered, we deeply investigated the most novel blood vessel segmentation including machine learning, deformable model, and tracking-based approaches.
Methods
This paper analyzes more than 100 articles focused on blood vessel segmentation methods. For each analyzed approach, summary tables are presented reporting imaging technique used, anatomical region and performance measures employed. Benefits and disadvantages of each method are highlighted.
Discussion
Despite the constant progress and efforts addressed in the field, several issues still need to be overcome. A relevant limitation consists in the segmentation of pathological vessels. Unfortunately, not consistent research effort has been addressed to this issue yet. Research is needed since some of the main assumptions made for healthy vessels (such as linearity and circular cross-section) do not hold in pathological tissues, which on the other hand require new vessel model formulations. Moreover, image intensity drops, noise and low contrast still represent an important obstacle for the achievement of a high-quality enhancement. This is particularly true for optical imaging, where the image quality is usually lower in terms of noise and contrast with respect to magnetic resonance and computer tomography angiography.
Conclusion
No single segmentation approach is suitable for all the different anatomical region or imaging modalities, thus the primary goal of this review was to provide an up to date source of information about the state of the art of the vessel segmentation algorithms so that the most suitable methods can be chosen according to the specific task.},
  keywords = {Blood vessels, Medical imaging, Review, Segmentation},
}

@InProceedings{10.1007/978-3-319-93000-8_71,
  author    = {Meyer, Maria Ines and Galdran, Adrian and Costa, Pedro and Mendon{\c{c}}a, Ana Maria and Campilho, Aur{\'e}lio},
  title     = {Deep Convolutional Artery/Vein Classification of Retinal Vessels},
  booktitle = {Image Analysis and Recognition},
  year      = {2018},
  pages     = {622--630},
  abstract  = {The classification of retinal vessels into arteries and veins in eye fundus images is a relevant task for the automatic assessment of vascular changes. This paper presents a new approach to solve this problem by means of a Fully-Connected Convolutional Neural Network that is specifically adapted for artery/vein classification. For this, a loss function that focuses only on pixels belonging to the retinal vessel tree is built. The relevance of providing the model with different chromatic components of the source images is also analyzed. The performance of the proposed method is evaluated on the RITE dataset of retinal images, achieving promising results, with an accuracy of {\$}{\$}96{\backslash}{\%}{\$}{\$}96{\%}on large caliber vessels, and an overall accuracy of {\$}{\$}84{\backslash}{\%}{\$}{\$}84{\%}.},
}

@Article{alam2018combining,
  author    = {Alam, Minhaj and Son, Taeyoon and Toslak, Devrim and Lim, Jennifer I and Yao, Xincheng},
  title     = {Combining ODR and blood vessel tracking for artery--vein classification and analysis in color fundus images},
  journal   = {Translational vision science \& technology},
  year      = {2018},
  volume    = {7},
  number    = {2},
  pages     = {23--23},
}

@Article{son2017retinal,
  author  = {Son, Jaemin and Park, Sang Jun and Jung, Kyu-Hwan},
  title   = {Retinal vessel segmentation in fundoscopic images with generative adversarial networks},
  journal = {arXiv preprint arXiv:1706.09318},
  year    = {2017},
}

@Article{guibas2017synthetic,
  author  = {Guibas, John T and Virdi, Tejpal S and Li, Peter S},
  title   = {Synthetic medical images from dual generative adversarial networks},
  journal = {arXiv preprint arXiv:1709.01872},
  year    = {2017},
}

@Article{8055572,
  author   = {P. {Costa} and A. {Galdran} and M. I. {Meyer} and M. {Niemeijer} and M. {Abràmoff} and A. M. {Mendonça} and A. {Campilho}},
  title    = {End-to-End Adversarial Retinal Image Synthesis},
  journal  = {IEEE Transactions on Medical Imaging},
  year     = {2018},
  volume   = {37},
  number   = {3},
  pages    = {781-791},
  keywords = {blood vessels;eye;image colour analysis;image segmentation;learning (artificial intelligence);medical image processing;probability;retinal recognition;statistical distributions;medical image analysis applications;annotated medical data;retinal color images;adversarial learning;generative model;adversarial autoencoder;retinal vessel network synthesis;generated vessel trees;color retinal images;generative adversarial network;differentiable loss functions;end-to-end retinal image synthesis system;data points;synthesized images;vessel networks;latent space learning;end-to-end adversarial retinal image synthesis;probability distribution;Training;Biomedical imaging;Image generation;Retinal vessels;Mathematical model;Retinal image synthesis;retinal image analysis;generative adversarial networks;adversarial autoencoders},
}

@Article{costa2017towards,
  author  = {Costa, Pedro and Galdran, Adrian and Meyer, Maria In{\^e}s and Abr{\`a}moff, Michael David and Niemeijer, Meindert and Mendon{\c{c}}a, Ana Maria and Campilho, Aur{\'e}lio},
  title   = {Towards adversarial retinal image synthesis},
  journal = {arXiv preprint arXiv:1701.08974},
  year    = {2017},
}

@Article{staal:2004-855,
  author  = {J.J. Staal AND M.D. Abramoff AND M. Niemeijer AND M.A. Viergever AND B. van Ginneken},
  title   = {{Ridge based vessel segmentation in color images of the retina}},
  journal = {{IEEE Transactions on Medical Imaging}},
  year    = {2004},
  volume  = {23},
  number  = {4},
  pages   = {501-509},
}

@InProceedings{10.1007/978-3-642-40763-5_54,
  author    = {Hu, Qiao and Abr{\`a}moff, Michael D. and Garvin, Mona K.},
  title     = {Automated Separation of Binary Overlapping Trees in Low-Contrast Color Retinal Images},
  booktitle = {MICCAI 2013},
  year      = {2013},
  pages     = {436--443},
  abstract  = {While many approaches exist for the automated segmentation of retinal vessels in fundus photographs, limited work has focused on the problem of separating the arterial from the venous trees. The few existing approaches that do exist for separating arteries from veins are local and/or greedy in nature, making them susceptible to errors or limiting their applicability to only the very largest vessels. In this work, we propose a new, more global, optimization framework for separating two overlapping trees within medical images and apply this approach for the separation of arteriovenous trees in low-contrast color fundus images. In particular, our approach has two stages. The first stage is to generate a vessel potential connectivity map (VPCM) consisting of vessel segments and the potential connectivity between them. The second stage is to separate the VPCM into multiple anatomical trees using a graph-based meta-heuristic algorithm. Based on a graph model, the algorithm first uses local knowledge and global constraints of the vasculature to generate near-optimal candidate solutions, and then obtains the final solution based on global costs. We test the algorithm on 48 low-contrast fundus images and the promising results suggest its applicability and robustness.},
}

@Article{radford2015unsupervised,
  author  = {Radford, Alec and Metz, Luke and Chintala, Soumith},
  title   = {Unsupervised representation learning with deep convolutional generative adversarial networks},
  journal = {arXiv preprint arXiv:1511.06434},
  year    = {2015},
}

@Article{chatziralli2012value,
  author    = {Chatziralli, Irini P and Kanonidou, Evgenia D and Keryttopoulos, Petros and Dimitriadis, Prodromos and Papazisis, Leonidas E},
  title     = {The value of fundoscopy in general practice},
  journal   = {The open ophthalmology journal},
  year      = {2012},
  volume    = {6},
  pages     = {4},
}

@InProceedings{rodkaew2002algorithm,
  author    = {Rodkaew, Yodthong and Siripant, Suchada and Lursinsap, Chidchanok and Chongstitvatana, Prabhas},
  title     = {An algorithm for generating vein images for realistic modeling of a leaf},
  booktitle = {Prodeedings of the international conference on computational mathematics and modeling},
  year      = {2002},
  volume    = {9},
}

@InProceedings{Bastani_2018_CVPR,
  author    = {Bastani, Favyen and He, Songtao and Abbar, Sofiane and Alizadeh, Mohammad and Balakrishnan, Hari and Chawla, Sanjay and Madden, Sam and DeWitt, David},
  title     = {RoadTracer: Automatic Extraction of Road Networks From Aerial Images},
  booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year      = {2018},
}

@Article{AZZOPARDI201546,
  author   = {George Azzopardi and Nicola Strisciuglio and Mario Vento and Nicolai Petkov},
  title    = {Trainable COSFIRE filters for vessel delineation with application to retinal images},
  journal  = {Medical Image Analysis},
  year     = {2015},
  volume   = {19},
  number   = {1},
  pages    = {46 - 57},
  abstract = {Retinal imaging provides a non-invasive opportunity for the diagnosis of several medical pathologies. The automatic segmentation of the vessel tree is an important pre-processing step which facilitates subsequent automatic processes that contribute to such diagnosis. We introduce a novel method for the automatic segmentation of vessel trees in retinal fundus images. We propose a filter that selectively responds to vessels and that we call B-COSFIRE with B standing for bar which is an abstraction for a vessel. It is based on the existing COSFIRE (Combination Of Shifted Filter Responses) approach. A B-COSFIRE filter achieves orientation selectivity by computing the weighted geometric mean of the output of a pool of Difference-of-Gaussians filters, whose supports are aligned in a collinear manner. It achieves rotation invariance efficiently by simple shifting operations. The proposed filter is versatile as its selectivity is determined from any given vessel-like prototype pattern in an automatic configuration process. We configure two B-COSFIRE filters, namely symmetric and asymmetric, that are selective for bars and bar-endings, respectively. We achieve vessel segmentation by summing up the responses of the two rotation-invariant B-COSFIRE filters followed by thresholding. The results that we achieve on three publicly available data sets (DRIVE: Se=0.7655, Sp=0.9704; STARE: Se=0.7716, Sp=0.9701; CHASE_DB1: Se=0.7585, Sp=0.9587) are higher than many of the state-of-the-art methods. The proposed segmentation approach is also very efficient with a time complexity that is significantly lower than existing methods.},
  keywords = {COSFIRE, Delineation, Retinal image analysis, Trainable filters, Vessel segmentation},
}

@Article{6848752,
  author   = {S. {Roychowdhury} and D. D. {Koozekanani} and K. K. {Parhi}},
  title    = {Blood Vessel Segmentation of Fundus Images by Major Vessel Extraction and Subimage Classification},
  journal  = {IEEE Journal of Biomedical and Health Informatics},
  year     = {2015},
  volume   = {19},
  number   = {3},
  pages    = {1118-1128},
  keywords = {biomedical optical imaging;blood vessels;eye;feature extraction;Gaussian processes;high-pass filters;image classification;image enhancement;image reconstruction;image segmentation;medical image processing;mixture models;blood vessel segmentation;fundus images;major vessel extraction;subimage classification;fundus photographs;high-pass filtering;binary image;morphologically reconstructed enhanced image;Gaussian mixture model classifier;GMM;features extraction;pixel neighborhood;first-order gradient images;second-order gradient images;DRIVE dataset;STARE dataset;CHASE_DB1 dataset;Image segmentation;Biomedical imaging;Blood vessels;Feature extraction;Retina;Image reconstruction;Training data;Classification;feature selection;fundus images;high-pass filter;morphological reconstruction;peripapillary vessel;vessel segmentation;Algorithms;Databases, Factual;Diagnostic Techniques, Ophthalmological;Fundus Oculi;Humans;Image Processing, Computer-Assisted;Normal Distribution;Retinal Vessels},
}

@Article{7161344,
  author   = {Q. {Li} and B. {Feng} and L. {Xie} and P. {Liang} and H. {Zhang} and T. {Wang}},
  title    = {A Cross-Modality Learning Approach for Vessel Segmentation in Retinal Images},
  journal  = {IEEE Transactions on Medical Imaging},
  year     = {2016},
  volume   = {35},
  number   = {1},
  pages    = {109-118},
  keywords = {eye;image segmentation;medical image processing;neural nets;cross-modality learning approach;vessel segmentation;retinal images;vessel map;neural network;induction ability;center pixel;cross-training evaluation;image diagnosis;ophthalmologic diseases;computing framework;image segmentation;Image segmentation;Retina;Training;Neural networks;Accuracy;Feature extraction;Deformable models;Cross-modality learning;deep learning;retinal image;vessel segmentation;Algorithms;Databases, Factual;Humans;Image Processing, Computer-Assisted;Machine Learning;Retinal Vessels},
}

@Article{hu2015automated,
  author    = {Hu, Qiao and Abr{\`a}moff, Michael D and Garvin, Mona KK},
  title     = {Automated construction of arterial and venous trees in retinal images},
  journal   = {Journal of Medical Imaging},
  year      = {2015},
  volume    = {2},
  number    = {4},
  pages     = {044001},
}

@Article{5876322,
  author   = {M. {Niemeijer} and X. {Xu} and A. V. {Dumitrescu} and P. {Gupta} and B. {van Ginneken} and J. C. {Folk} and M. D. {Abramoff}},
  title    = {Automated Measurement of the Arteriolar-to-Venular Width Ratio in Digital Color Fundus Photographs},
  journal  = {IEEE Transactions on Medical Imaging},
  year     = {2011},
  volume   = {30},
  number   = {11},
  pages    = {1941-1950},
  month    = {Nov},
  keywords = {biomedical optical imaging;blood vessels;diseases;eye;medical image processing;automated measurement;arteriolar-to-venular width ratio;digital color fundus photograph;retinal arteries;retinal veins;cerebral atrophy;stroke;cardiovascular events;disease;retinopathy;optic disc;vessel segmentation;vessel width determination;skeletonization operation;bifurcation points;Optical imaging;Veins;Arteries;Image segmentation;Eyes;Feature extraction;Artery;artery vein ratio;artery vein separation;retina;vasculature;vein;Adult;Color;Diabetic Retinopathy;Diabetic Retinopathy;Fundus Oculi;Humans;Hypertensive Retinopathy;Hypertensive Retinopathy;Hypertensive Retinopathy;Optic Disk;Optic Disk;Photography;Radiographic Image Interpretation, Computer-Assisted;Reference Standards;Retina;Retina;Retinal Artery;Retinal Artery;Retinal Artery;Retinal Vein;Retinal Vein;Retinal Vein;Retinal Vessels;Retinal Vessels;Vascular Resistance},
}

@Article{kingma2014adam,
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    journal={arXiv preprint arXiv:1412.6980},
}

@inproceedings{orlando2018towards,
  title={Towards a glaucoma risk index based on simulated hemodynamics from fundus images},
  author={Orlando, Jos{\'e} Ignacio and Breda, Jo{\~a}o Barbosa and Van Keer, Karel and Blaschko, Matthew B and Blanco, Pablo J and Bulant, Carlos A},
  booktitle={MICCAI},
  pages={65--73},
  year={2018}
}

@Comment{jabref-meta: databaseType:bibtex;}
